News

IEEE4d
A Preference-Based Multi-Agent Federated Reinforcement Learning Algorithm Framework for Trustworthy Interactive Urban Autonomous Driving - IEEE Xplore
In autonomous driving (AD) tasks, data-driven deep reinforcement learning (DRL) outperforms rule-based methods in terms of continuous decision-making and adaptability. However, traditional DRL relies ...
IEEE7d
Integrating Machine Learning Algorithms With Quantum Annealing Solvers for Online Fraud Detection - IEEE Xplore
Machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems detect duplicitous activities after they have already occurred, not at or ...
Security Boulevard15d
Zero Trust Security Model: Key Strategies, Benefits, and Implementation Best Practices
Components of a Zero Trust Framework Identity and Access Management (IAM) Verify the user and the device before they access anything. But it goes beyond just Multi-Factor Authentication (MFA). A ...
Security Boulevard21d
Why a Layered Approach Is Essential for Cybersecurity and Zero Trust
Today’s cybersecurity landscape is complex and unforgiving. Remote work, Saas, AI Agents, cloud migration, and ever-evolving cyber threats have exposed the limitations of relying on standalone ...
Electronic Design24d
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Infosecurity-magazine.com26d
NIST Publishes New Zero Trust Implementation Guidance
Zero Trust Implementation Options The NIST guidance offers 19 example implementations of ZTAs built using commercial, off-the-shelf technologies. These were developed through a project at the NIST ...
Wired26d
A Deep Learning Alternative Can Help AI Agents Gameplay the Real World
A Deep Learning Alternative Can Help AI Agents Gameplay the Real World A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.